Seeking Interpretable Models for High-Dimensional Data
主 题: Seeking Interpretable Models for High-Dimensional Data
报告人: Prof. Bin Yu (University of California, Berkeley)
时 间: 2009-09-24 下午15:00
地 点: 理科一号楼 1560
Information technology has enabled collection of massive amounts of data in science, engineering, social science, finance and beyond. Statistics is the science of data. Extracting useful information from these high-dimensional data is the focus of today's statistical research and practice. After broad success of statistical machine learning on prediction through regularization, interpretability is gaining attention and sparsity has been used as its proxy. With the virtues of both regularization and sparsity, L1 penalized empirical minimization (e.g. Lasso) has been very popular recently.
In this talk, I would like to cover both theory and practice of L1 penalized minimization. First, I will give a brief overview of recent theoretical results on model selection consistency (when p>>n) of Lasso and graphical Lasso. Second, I will present on-going collaborative research with the Gallant Lab at
Berkeley
on understanding visual pathway. In particular, sparse models (linear, non-linear, and graphical) have been built to relate natural images to fMRI responses in human primary visual cortex area V1. Issues of model validation will be discussed.
